Sea Level Rise - Scarcies, Sierra Leone

In this project, we will be analyzing the impacts of sea level rise in the Scarcies region of North Sierra Leone using data from the Digital Earth Africa program.

Visualize the data

The following image is a screenshot showing the visualization of the data through the interactive map displayed below.

The map below has the following features:

  • Area of interest in yellow.

  • Data points are represented by different colors, which indicate the shoreline behavior over time. The color determination and count per cluster can be found in [###Table 1].

  • The extent of mangroves is depicted in the color green.

  • To navigate the map and select specific layers, you can use the interactive buttons provided.

  • In the left-hand section, there is a “layers button” that you can use to navigate to the different data sets.

  • Removing the “World” layer helps enhance the level of detail in the satellite images provided by the tmap() function.

Important Insights

  • Complicated coastline and model: The coastline and the model used to define shoreline distances are complex due to various environmental factors such as currents, deltas, and islands.

  • Extreme values: Values greater than 200 meters are more likely to indicate modeling issues rather than real-world coastal change.

  • Noisy data points: The dataset contains 3,920 points categorized as noisy. Additionally, in the year 2001, there are 1,398 fuzzy data points, which have been removed entirely.

  • Median values per year: Using the median values, a linear model suggests an erosion rate of 0.304 meters per year, indicating a potential loss or erosion of 30.4 centimeters annually.

  • Mean values per year: Running a linear model on the mean values indicates that for every one unit increase in year, the mean shoreline distance is projected to grow by 1.4 meters.

Questions:

  1. Focus on erosion data points: Should the focus be on data points that show erosion related to sea-level rise, rather than a broader shoreline analysis?

  2. Correlation with mangrove presence: Are we interested on the correlation between shoreline movement and the presence of mangroves?

  3. Winsorize function for outliers: Should the Winsorize() function be used to control outliers by setting maximum and minimum points for shoreline erosion?

Data Appendices

Table 1

kable(color_table,
      caption = "Table 1. Data Points Color Breaks") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Table 1. Data Points Color Breaks
colors lower_bound upper_bound count
darkred -Inf -5.00 110
red -5.00 -1.00 514
pink -1.00 -0.25 1033
white -0.25 0.00 712
white 0.00 0.25 712
lightblue 0.25 1.00 407
blue 1.00 5.00 288
darkblue 5.00 Inf 173

Histograms

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 6 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 6 rows containing non-finite values (`stat_bin()`).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 4 rows containing non-finite values (`stat_bin()`).

The values represent the distance from the shoreline of each respective year to the shoreline position in 2021. Negative values indicate that the shoreline was further inland compared to 2021, suggesting shoreline growth. On the other hand, positive values indicate that the shoreline was further erosion or the movement of the shoreline further away from the reference point over time.

OLS Model

## 
## Call:
## lm(formula = median ~ year, data = .)
## 
## Coefficients:
## (Intercept)         year  
##    617.0338      -0.3042
## 
## Call:
## lm(formula = mean ~ year, data = .)
## 
## Coefficients:
## (Intercept)         year  
##   -2821.586        1.398

Regarding the Median model, the slope of -0.3 implies that with a one-unit rise in the year, the Median is likely to drop by 0.3 units. This indicates that the land might lose or erode 0.304 meters. Conversely, the slope of the Mean model is 1.4, which indicates that for every one unit increase in the year, the Mean is projected to grow by 1.4.

Linear Model Graph

Annual Shoreline Change Rates

Webpage vs. OLS Analysis by Year

This code is comparing the rate_time values obtained from a webpage with those obtained from an OLS analysis for each year, to identify any differences between the two.

Table 2

Table 2. Comparison of the First Six Data Points
uid rate_time linear_slope
e9wd7gce4v -0.49 -0.5390554
e9wd7gcfch -0.34 -0.3204402
e9wd7gcfxk -0.40 -0.3646631
e9wd7gexz4 -1.21 -1.2477487
e9wd7gezt6 -1.03 -1.0639324
e9wd7gf3ge -0.89 -0.8567742 
## [1] 0.2033859
## [1] 0.1919374

This table and results provides a comparison between the rate_time variable obtained from the digital analysis and the manually computed linear model. In the presented table, the first 6 rows of results exhibit the rate_time value from Digital Africa and the linear_slope value calculated manually. To evaluate the model’s overall fit, I computed the mean rate_time value, which equaled 0.2033 meters. Furthermore, the linear slope analysis generated a value of 0.2199, which closely aligns with the mean rate_time.